AI Based Early Flood Monitoring and Detection

Advanced technology solutions are necessary in light of the increasing natural disasters that are linked to climate change, such as unpredictable rainfall and flooding. Automation, as a key technology, emerges to streamline processes with minimal human intervention. This paper investigates existing flood monitoring systems, recognising the critical impact of flooding on communities residing in vulnerable areas. The primary objective is to contribute to the existing literature by developing an automated flood monitoring system as an extension of the review process. The proposed system seeks to integrate automation into a community-based early warning system, providing continuous water level updates through an LCD display. The focus is on preemptive measures to mitigate the adverse effects of floods by implementing timely warnings. The flood early warning system is designed to alert residents through warning messages prior to the occurrence of floods. This encompasses a comprehensive overview of community-based early warning systems, involving a survey of such systems in a specific region. The initial phase involves an extensive literature review covering IoT-based hardware and software, as well as early flood warning systems. Subsequently, a small-scale working model of an early flood warning system has been developed utilising IoT-based hardware and software. The model incorporates float and flow sensors as sensing devices to gauge water level and flow, transmitting this data to a microprocessor. The microprocessor processes the information and relays the output to a water level indicator, serving as an early warning mechanism.